Effective segmentation and classification of thyroid histopathology images

Graphical abstractDisplay Omitted This paper proposes a Computer Aided Diagnosis (CAD) system that semi-automatically segments and classifies H&E-stained thyroid histopathology images into two classes: Normal Thyroid (NT) or Papillary Thyroid Carcinoma (PTC) based on nuclear texture features. Our system segments the given histopathology image into different binary images using Particle Swarm Optimization (PSO)-based Otsu's multilevel thresholding. From the segmented binary images, a binary image containing the nuclei is chosen manually. Nuclei are extracted from the manually selected binary image by imposing an area constraint and a roundness constraint. The intensity variations of pixels within the nuclei are quantified by extracting texture features. Variable Precision Rough Sets (VPRS)-based β-reduct is used to identify redundant features and generate rules. The rules are then stored in a rule base. A novel closest-matching-rule (CMR) algorithm is proposed to classify a new test sample as PTC or NT using the rules in the rule base. We verified experimentally that the proposed CAD system provides promising results and it is supposed to assist pathologists in their decisions.

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